A Resilience Approach to Improving Safety Performance in Construction
Bibliographic record
Abstract
The construction industry is recognized as having inherent risks with high levels of change and uncertainty.The complexity of this project poses a challenge to traditional safety management approaches.As an alternative, a resilience approach to traditional safety management is used, which is designed to deal with uncertainty in a high-risk work environment in the form of a resilience safety culture.This research will look at the relationship between resilience safety culture variables, and safety performance in a stateowned construction company in Indonesia using the partial least squares structural equation modeling approach.Resilience safety culture variables in this study include management commitment, reporting, learning, anticipation, flexibility, awareness, coworker's safety perception, supervisor's safety perception, safety attitudes, understanding of risk, safety resources, and safety procedures.The results of the PLS-SEM test show that there are 9 relationships between variables that have a significant positive influence.As a significant variable that has a direct effect on safety performance, awareness is the main determining factor for improving the company's safety performance.With the significant influence of safety awareness on safety performance, there is a need to increase safety awareness to ensure consistent improvement in safety performance.It is recommended for companies to be able to increase safety awareness, one of which is through safety awareness workshops as a way to improve safety performance.The provision of this workshop is an initiative to increase the ability to anticipate and manage risk proactively which can result in an increased level of awareness of hazards and provide a deeper understanding of safety at work sites so that appropriate preventive measures can be taken to reduce the possibility of accidents or unwanted incidents.These results are expected to be used as a guideline for companies in increasing their safety performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".